






"""simple docstring"""





from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


_SCREAMING_SNAKE_CASE   :							Any          =		logging.get_logger(__name__)






class 	a		(   __snake_case						):
    SCREAMING_SNAKE_CASE		:	int		=					["""audio_values""", """audio_mask"""]






    def __init__(			self	:				int			,		__SCREAMING_SNAKE_CASE	:				str=2048			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]=1			,		__SCREAMING_SNAKE_CASE	:				Dict=[16, 16]			,		__SCREAMING_SNAKE_CASE	:				Optional[int]=128			,		__SCREAMING_SNAKE_CASE	:				int=44100			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]=86			,		__SCREAMING_SNAKE_CASE	:				Any=2048			,		__SCREAMING_SNAKE_CASE	:				List[Any]=0.0			,		**__SCREAMING_SNAKE_CASE	:				int			,		)	->						Optional[int]:
      super().__init__(
          feature_size=__SCREAMING_SNAKE_CASE			,		sampling_rate=__SCREAMING_SNAKE_CASE			,		padding_value=__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE			,		)

      lowerCamelCase_       	=		spectrogram_length
      lowerCamelCase_       	=		num_channels
      lowerCamelCase_       	=		patch_size
      lowerCamelCase_       	=		feature_size // self.patch_size[1]
      lowerCamelCase_       	=		n_fft
      lowerCamelCase_       	=		sampling_rate // hop_length_to_sampling_rate
      lowerCamelCase_       	=		sampling_rate
      lowerCamelCase_       	=		padding_value
      lowerCamelCase_       	=		mel_filter_bank(
          num_frequency_bins=1 + n_fft // 2			,		num_mel_filters=__SCREAMING_SNAKE_CASE			,		min_frequency=0.0			,		max_frequency=22_050.0			,		sampling_rate=__SCREAMING_SNAKE_CASE			,		norm='slaney'			,		mel_scale='slaney'			,		).T






    def   UpperCamelCase			(			self	:				Dict			,		__SCREAMING_SNAKE_CASE	:				np.array   )	->						np.ndarray:
      lowerCamelCase_       	=		spectrogram(
          __SCREAMING_SNAKE_CASE			,		window_function(self.n_fft			,		'hann'   )			,		frame_length=self.n_fft			,		hop_length=self.hop_length			,		power=2.0			,		mel_filters=self.mel_filters.T			,		log_mel='dB'			,		db_range=80.0			,		)
      lowerCamelCase_       	=		log_spec[:, :-1]
      lowerCamelCase_       	=		log_spec - 20.0
      lowerCamelCase_       	=		np.clip(log_spec / 40.0			,		-2.0			,		0.0   ) + 1.0
      return log_spec






    def __call__(			self	:				str			,		__SCREAMING_SNAKE_CASE	:				Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]			,		__SCREAMING_SNAKE_CASE	:				Optional[Union[str, TensorType]] = None			,		__SCREAMING_SNAKE_CASE	:				Optional[bool] = True			,		__SCREAMING_SNAKE_CASE	:				Optional[int] = None			,		__SCREAMING_SNAKE_CASE	:				bool = False			,		__SCREAMING_SNAKE_CASE	:				bool = False			,		**__SCREAMING_SNAKE_CASE	:				List[str]			,		)	->						BatchFeature:

      if sampling_rate is not None:
        if sampling_rate != self.sampling_rate:
          raise ValueError(
              'This feature extractor is set to support sampling rate'
              F''' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'''
              F''' with {self.sampling_rate} and not {sampling_rate}.'''   )
      else:
        logger.warning(
            'It is strongly recommended to pass the `sampling_rate` argument to this function. '
            'Failing to do so can result in silent errors that might be hard to debug.'   )

      lowerCamelCase_       	=		isinstance(__SCREAMING_SNAKE_CASE			,		np.ndarray   ) and len(raw_speech.shape   ) > 1
      if is_batched_numpy and len(raw_speech.shape   ) > 2:
        raise ValueError(F'''Only mono-channel audio is supported for input to {self}'''   )
      lowerCamelCase_       	=		is_batched_numpy or (
          isinstance(__SCREAMING_SNAKE_CASE			,		(list, tuple)   ) and (isinstance(raw_speech[0]			,		(np.ndarray, tuple, list)   ))
      )
      if is_batched:
        lowerCamelCase_       	=		[np.asarray([speech]			,		dtype=np.floataa   ).T for speech in raw_speech]
      elif not is_batched and not isinstance(__SCREAMING_SNAKE_CASE			,		np.ndarray   ):
        lowerCamelCase_       	=		np.asarray(__SCREAMING_SNAKE_CASE			,		dtype=np.floataa   )
      elif isinstance(__SCREAMING_SNAKE_CASE			,		np.ndarray   ) and raw_speech.dtype is np.dtype(np.floataa   ):
        lowerCamelCase_       	=		raw_speech.astype(np.floataa   )
      # always return batch
      if not is_batched:
        lowerCamelCase_       	=		[np.asarray([raw_speech]   ).T]

      # Convert audio signals to log mel spectrograms, truncate by time axis
      lowerCamelCase_       	=		[
          self._np_extract_fbank_features(waveform.squeeze()   ).T[: self.spectrogram_length] for waveform in raw_speech
      ]
      if isinstance(audio_features[0]			,		__SCREAMING_SNAKE_CASE   ):
        lowerCamelCase_       	=		[np.asarray(__SCREAMING_SNAKE_CASE			,		dtype=np.floataa   ) for feature in audio_features]

      # Create audio attention mask
      lowerCamelCase_       	=		max(
          [ceil(feature.shape[0] / self.patch_size[0]   ) * self.freq_len for feature in audio_features]   )  # The maximum number of audio patches in a batch
      if return_attention_mask:
        lowerCamelCase_       	=		[
            (ceil(feature.shape[0] / self.patch_size[0]   ) * self.freq_len) * [1]
            + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]   ) * self.freq_len) * [0]
            for feature in audio_features
        ]
        lowerCamelCase_       	=		np.array(__SCREAMING_SNAKE_CASE   ).astype(np.floataa   )

      # convert into correct format for padding
      lowerCamelCase_       	=		max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
      lowerCamelCase_       	=		np.ones([len(__SCREAMING_SNAKE_CASE   ), 1, max_time_len, self.feature_size]   ).astype(np.floataa   )
      lowerCamelCase_       	=		padded_audio_features * self.padding_value
      for i in range(len(__SCREAMING_SNAKE_CASE   )   ):
        lowerCamelCase_       	=		audio_features[i]
        lowerCamelCase_       	=		feature

      # return as BatchFeature
      if return_attention_mask:
        lowerCamelCase_       	=		{'audio_values': padded_audio_features, 'audio_mask': audio_mask}
      else:
        lowerCamelCase_       	=		{'audio_values': padded_audio_features}

      lowerCamelCase_       	=		BatchFeature(data=__SCREAMING_SNAKE_CASE			,		tensor_type=__SCREAMING_SNAKE_CASE   )
      return encoded_inputs












"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE   :							List[Any]          =		{'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		['''PLBartTokenizer''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE   :							List[str]          =		[
      '''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''PLBartForCausalLM''',
      '''PLBartForConditionalGeneration''',
      '''PLBartForSequenceClassification''',
      '''PLBartModel''',
      '''PLBartPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_plbart import PLBartTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_plbart import (
        PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
        PLBartForCausalLM,
        PLBartForConditionalGeneration,
        PLBartForSequenceClassification,
        PLBartModel,
        PLBartPreTrainedModel,
    )


else:
  import sys

  _SCREAMING_SNAKE_CASE   :							Optional[Any]          =		_LazyModule(__name__, globals()['''__file__'''], _import_structure)




